DATA MINING AND STAT LEARN CERTIFICATION
EVALUATION 2026 SOLVED QUESTIONS
◉ Column. Answer: Attribute, feature, covariate, predictor, factor,
variable
◉ Response/outcome. Answer: The 'answer' for each data point
◉ Structured data. Answer: Data that can be stored in a structured
way
◉ Quantitative. Answer: Numbers with meaning
◉ Categorical. Answer: Numbers without meaning
◉ Binary data. Answer: Can only take one of two values
◉ Unstructured data. Answer: Data that is not easily described and
stored
◉ Unrelated data. Answer: No relationship between data points
, ◉ Time series data. Answer: Same data recorded over time
◉ Validation. Answer: Measuring the quality of a model
◉ Real effect. Answer: Real relationship between attributes and
response, same in all data sets
◉ Random effect. Answer: Random, but looks like a real effect,
different in all data sets
◉ Model fit. Answer: Captures real and random effects
◉ Heuristic. Answer: Fast, good but not guaranteed to find absolute
best solution
◉ Expectation-maximization (EM). Answer: An iterative method to
find maximum likelihood or maximum a posteriori estimates of
parameters in statistical models
◉ Supervised learning. Answer: Correct answer (response) is known
for each data point (Ex: Classification)
◉ Unsupervised learning. Answer: Correct answer (response) is not
known (Ex: Clustering)
EVALUATION 2026 SOLVED QUESTIONS
◉ Column. Answer: Attribute, feature, covariate, predictor, factor,
variable
◉ Response/outcome. Answer: The 'answer' for each data point
◉ Structured data. Answer: Data that can be stored in a structured
way
◉ Quantitative. Answer: Numbers with meaning
◉ Categorical. Answer: Numbers without meaning
◉ Binary data. Answer: Can only take one of two values
◉ Unstructured data. Answer: Data that is not easily described and
stored
◉ Unrelated data. Answer: No relationship between data points
, ◉ Time series data. Answer: Same data recorded over time
◉ Validation. Answer: Measuring the quality of a model
◉ Real effect. Answer: Real relationship between attributes and
response, same in all data sets
◉ Random effect. Answer: Random, but looks like a real effect,
different in all data sets
◉ Model fit. Answer: Captures real and random effects
◉ Heuristic. Answer: Fast, good but not guaranteed to find absolute
best solution
◉ Expectation-maximization (EM). Answer: An iterative method to
find maximum likelihood or maximum a posteriori estimates of
parameters in statistical models
◉ Supervised learning. Answer: Correct answer (response) is known
for each data point (Ex: Classification)
◉ Unsupervised learning. Answer: Correct answer (response) is not
known (Ex: Clustering)
